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Health Ready Components are essential to unlocking the potential of Integrated Vehicle Health Management (IVHM) as it relates to real-time diagnosis and prognosis in order to achieve lower maintenance costs, greater asset availability, reliability and safety. IVHM results in reduced maintenance costs by providing more accurate fault isolation and repair guidance. IVHM results in greater asset availability, reliability and safety by recommending preventative maintenance and by identifying anomalous behavior indicative of degraded functionality prior to detection of the fault by other detection mechanisms. The cost, complexity and effectiveness of the IVHM system design, deployment and support depend, to a great extent, on the degree to which components and subsystems provide the run-time data needed by IVHM and the design time semantic data to allow IVHM to interpret those messages.

To cope with the increasing number of advanced features (e.g., smart-phone integration and side-blind zone alert.) being deployed in vehicles, automotive manufacturers are designing flexible hardware architectures which can accommodate increasing feature content with as fewer as possible hardware changes so as to keep future costs down. In this paper, we propose a formal and quantitative definition of flexibility, a related methodology and a tool flow aimed at maximizing the flexibility of an automotive hardware architecture with respect to the features that are of greater importance to the designer. We define flexibility as the ability of an architecture to accommodate future changes in features with no changes in hardware (no addition/replacement of processors, buses, or memories). We utilize an optimization framework based on mixed integer linear programming (MILP) which computes the flexibility of the architecture while guaranteeing performance and safety requirements.

This paper presents some of the challenges and successful outcomes in developing the aerodynamic characteristics of the Chevrolet Volt, an electric vehicle with an extended-range capability. While the Volt's propulsion system doesn't directly affect its shape efficiency, it does make aerodynamics much more important than in traditional vehicles. Aerodynamic performance is the second largest contributor to electric range, behind vehicle mass. Therefore, it was critical to reduce aerodynamic drag as much as possible while maintaining the key styling cues from the original concept car. This presented a number of challenges during the development, such as evaluating drag due to underbody features, balancing aerodynamics with wind noise and cooling flow, and interfacing with other engineering requirements. These issues were resolved by spending hundreds of hours in the wind tunnel and running numerous Computational Fluid Dynamics (CFD) analyses.

Understanding the flow characteristics and, especially, how the aerodynamic forces are influenced by the changes in the vehicle body shape, are very important in order to improve vehicle aerodynamics. One specific goal of aerodynamic shape optimization is to predict the local shape sensitivities for aerodynamic forces. The availability of a reliable and efficient sensitivity analysis method will help to reduce the number of design iterations and the aerodynamic development costs. Among various shape optimization methods, the Adjoint Method has received much attention as an efficient sensitivity analysis method for aerodynamic shape optimization because it allows the computation of sensitivity information for a large number of shape parameters simultaneously.

Engineering has continuously strived to improve the vehicle development process to achieve high quality designs and quick to launch products. The design process has to have the tools and capabilities to help ensure both quick to the market product and a flawless launch. To achieve high fidelity and robust design, mistakes and other quality issues must be addressed early in the engineering process. One way to detect problems early is to use the math based modeling and simulation techniques of the analysis group. The correlation of the actual vehicle performance to the predictive model is crucial to obtain. Without high correlation, the change management process begins to get complicated and costs start to increase exponentially. It is critical to reduce and eliminate the risk in a design up front before tooling begins to kick off. The push to help achieve a high rate of correlation has been initiated by engineering management, seeing this as an asset to the business.

Current manufacture of alternative energy sources for automobiles, such as fuel cells and lithium-ion batteries, uses repeating energy modules to achieve targeted balances of power and weight for varying types of vehicles. Specifically for lithium-ion batteries, tens to hundreds of identical plastic parts are assembled in a repeating fashion; this assembly of parts requires complex dimensional planning and high degrees of quality control. This paper will address the aspects of dimensional quality for repeated, injection molded thermoplastic battery components and will include the following: First, dimensional variation associated with thermoplastic components is considered. Sources of variation include the injection molding process, tooling or mold, lot-to-lot material differences, and varying types of environmental exposure. Second, mold tuning and cavity matching between molds for multi-cavity production will be analyzed.

Industry standards and practices define a number of mathematical and physical methods to estimate the cargo carrying volume capacity of a vehicle. While some have roots dating back decades, others try to assess the utility of the space for cargo by subjective measurements. Each these methods have their own inherent merits and deficiencies. The purpose of this paper is to highlight the differences in calculated cargo volume amongst the following practices: Society of Automobile Engineers (SAE) J1100[1] International Organization for Standardization (ISO 3832)[2], Global Car manufacturer's Information Exchange group (GCIE)[3], Consumer Reports[4]. This paper provides a method and associated rationale for constructing a new cargo volume calculation practice that attempts to harmonize these procedures into a more contiguous practice. This homologation will benefit publishing industry, vehicle manufacturers and customers alike.

In designing vehicles with significant electric driving range, optimizing vehicle energy efficiency is a key requirement to maximize the limited energy capacity of the onboard electrochemical energy storage system. A critical factor in vehicle energy efficiency is the vehicle mass. Optimizing mass allows for the possibility of either increasing electric driving range with a constant level of electrochemical energy storage or holding the range constant while reducing the level of energy storage, thus reducing storage cost. In this paper, a methodology is outlined to study the tradeoff between the battery cost savings achieved by vehicle mass reduction for a constant electric driving range and the cost associated with lightweighting a vehicle. This methodology enables informed business decisions about the available engineering options for lightweighting early in the vehicle development process. The methodology was applied to a compact extended-range electric vehicle (EREV) concept.

Roof racks are designed for carrying luggage during customers' travels. These rails need to be strong enough to be able to carry the luggage weight as well as be able to withstand aerodynamic loads that are generated when the vehicle is travelling at high speeds on highways. Traditionally, roof rail gage thickness is increased to account for these load cases (since these are manufactured by extrusion), but doing so leads to increased mass which adversely affects fuel efficiency. The current study focuses on providing the guidelines for strategically placing lightening holes and optimizing gage thickness so that the final design is robust to noise parameters and saves the most mass without adversely impacting wind noise performance while minimizing stress. The project applied Design for Six Sigma (DFSS) techniques to optimize roof rail parameters in order to improve the load carrying capacity while minimizing mass.

One of the keys to a good reliable design is evaluating potential and past issues, ascertaining and then mitigating the risk that past and future issues will potentially occur. This is even more important with the automobile designs of today and for those in the future, specifically the hybrid and fuel cell vehicle. DFMEA and FTA are tools that aid in the understanding of complex design risks, from the system level down to the component level. This session will look at different case studies (from simple to complex) and the strategies used to understand systemic failure modes using both DFMEA and FTA.

The GD₃ or GD Cubed method of problem prevention has been applied to product changes and to test results at the component, subsystem and vehicle level. GD₃ stands for Good Design - Good Discussion - Good Dissection. Good Discussion of changes (Design Review Based on Failure Mode) identifies BUDS of PROBLEMS that may arise from interfaces and areas of change. Good Dissection (Design Review Based on Test Results) is applied to physical test samples during and after tests to identify Buds of Problems that may not be obvious from inspection of the parts or test results. The paper first describes implementation of the GD₃ principles and methods supporting Good Discussion (DRBFM) and Good Dissection, and then discusses how they are applied and embedded in the Vehicle Development Process at General Motors Co.